from statistics import mean

import numpy as np





def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				0
				# Number of processes finished
				__lowercase					     =				0
				# Displays the finished process.
				# If it is 0, the performance is completed if it is 1, before the performance.
				__lowercase					     =				[0] * no_of_process
				# List to include calculation results
				__lowercase					     =				[0] * no_of_process

				# Sort by arrival time.
				__lowercase					     =				[burst_time[i] for i in np.argsort(lowercase				)]
				__lowercase					     =				[process_name[i] for i in np.argsort(lowercase				)]
				arrival_time.sort()

				while no_of_process > finished_process_count:
								__lowercase					     =				0
								while finished_process[i] == 1:
												i += 1
								if current_time < arrival_time[i]:
												__lowercase					     =				arrival_time[i]

								__lowercase					     =				0
								# Index showing the location of the process being performed
								__lowercase					     =				0
								# Saves the current response ratio.
								__lowercase					     =				0
								for i in range(0				,     lowercase				):
												if finished_process[i] == 0 and arrival_time[i] <= current_time:
																__lowercase					     =				(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
																    i
																]
												if response_ratio < temp:
																__lowercase					     =				temp
																__lowercase					     =				i

        # Calculate the turn around time
								__lowercase					     =				current_time + burst_time[loc] - arrival_time[loc]
								current_time += burst_time[loc]
								# Indicates that the process has been performed.
								__lowercase					     =				1
								# Increase finished_process_count by 1
								finished_process_count += 1

				return turn_around_time





def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				[0] * no_of_process
				for i in range(0				,     lowercase				):
								__lowercase					     =				turn_around_time[i] - burst_time[i]
				return waiting_time


if __name__ == "__main__":
		__a    :	Dict         =    5
		__a    :	str         =    ["""A""", """B""", """C""", """D""", """E"""]
		__a    :	Any         =    [1, 2, 3, 4, 5]
		__a    :	Union[str, Any]         =    [1, 2, 3, 4, 5]

		__a    :	str         =    calculate_turn_around_time(
		    process_name, arrival_time, burst_time, no_of_process
		)
		__a    :	Tuple         =    calculate_waiting_time(
		    process_name, turn_around_time, burst_time, no_of_process
		)

		print("""Process name \tArrival time \tBurst time \tTurn around time \tWaiting time""")
		for i in range(0, no_of_process):
				print(
				    F'''{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'''
				    F'''{turn_around_time[i]}\t\t\t{waiting_time[i]}'''
				)

		print(F'''average waiting time : {mean(waiting_time):.5f}''')
		print(F'''average turn around time : {mean(turn_around_time):.5f}''')
import datasets


__a    :	Optional[Any]         =    """\
@InProceedings{conneau2018xnli,
  author = \"Conneau, Alexis
                 and Rinott, Ruty
                 and Lample, Guillaume
                 and Williams, Adina
                 and Bowman, Samuel R.
                 and Schwenk, Holger
                 and Stoyanov, Veselin\",
  title = \"XNLI: Evaluating Cross-lingual Sentence Representations\",
  booktitle = \"Proceedings of the 2018 Conference on Empirical Methods
               in Natural Language Processing\",
  year = \"2018\",
  publisher = \"Association for Computational Linguistics\",
  location = \"Brussels, Belgium\",
}
"""

__a    :	Optional[Any]         =    """\
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
"""

__a    :	str         =    """
Computes XNLI score which is just simple accuracy.
Args:
    predictions: Predicted labels.
    references: Ground truth labels.
Returns:
    'accuracy': accuracy
Examples:

    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> xnli_metric = datasets.load_metric(\"xnli\")
    >>> results = xnli_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}
"""





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				return (preds == labels).mean()




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,_KWARGS_DESCRIPTION					)
class   _UpperCamelCase (		datasets.Metric					):


			"""simple docstring"""






			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   str:

							'''simple docstring'''


							return datasets.MetricInfo(
							    description=_DESCRIPTION ,  citation=_CITATION ,  inputs_description=_KWARGS_DESCRIPTION ,  features=datasets.Features(
							        {
							            '''predictions''': datasets.Value('''int64''' if self.config_name != '''sts-b''' else '''float32'''		),
							            '''references''': datasets.Value('''int64''' if self.config_name != '''sts-b''' else '''float32'''		),
							        }		) ,  codebase_urls=[] ,  reference_urls=[] ,  format='''numpy''' ,  )





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Dict:

							'''simple docstring'''


							return {"accuracy": simple_accuracy(lowerCAmelCase__ ,  lowerCAmelCase__		)}